1. Field of the Invention
The present invention relates, in general, to an apparatus and method for controlling the brightness of moving image signals in real time and, more particularly, to an apparatus and method for controlling the real time brightness of moving image signals, which applies an image processing technique of equalizing images using a cumulative distribution function, obtained by accumulating histogram information extracted from images, to image quality improvement fields for a moving image display device in real time, thus automatically correcting in real time the brightness of output images depending on the brightness of moving images that are input in real time, and outputting the corrected output images.
2. Description of the Related Art
Generally, a histogram equalization technique, which is a method of automatically adjusting the brightness of an image, is an image processing technique for converting a very dark image into a bright image and maximizing the contrast of an input image. This technique allows an image, which is difficult for a human to see, to be easily seen by improving the contrast of the image, so that the technique has been successfully applied to various fields.
However, in order to perform histogram equalization, as shown in FIG. 1, a histogram detection unit 20 reads image data stored in an image memory 10, and then calculates first the histogram distribution of an entire image. A cumulative distribution function calculation unit 30 accumulates the histogram distribution to obtain a cumulative distribution function. Thereafter, the cumulative distribution function is normalized to obtain a normalized cumulative distribution function suitable for mapping. The normalized cumulative distribution function is constructed in the form of a lookup table, so that a histogram-equalized image is obtained as an output signal if the cumulative distribution function is applied to an input image. As described above, the histogram equalization process is executed through a complicated algorithm. In most image processing, such a histogram equalization process has been executed in a software manner. Further, even though the histogram equalization is implemented using hardware, only a histogram generation part, which is the first processing step, is implemented using hardware, and the remaining data processing parts are implemented using software.
In this way, the histogram equalization process for still images can be implemented using software processing through the above-described technique, but 60 frames per second must be processed so as to apply the histogram equalization technique to moving images. Therefore, for such high-speed data processing, an entire algorithm needs to be implemented using hardware. However, there are several difficulties in implementing the above-described algorithm using hardware. That is, the histogram information of image data, which are input at very high speeds, must be calculated with respect to each frame, and cumulative distribution function data must be stored and used at the time of displaying images. At this time, in order to store a cumulative distribution function corresponding to one frame, at least 256-byte or greater histogram information should be stored in real time even though the stored histogram information differs with the number of image data bits and the size of screens. Further, according to the characteristics of moving image information, cumulative distribution functions can be normally calculated in the image input stage and applied in the display stage. Therefore at least three sets of cumulative distribution functions are stored. Accordingly, since a very large storage space is required to store cumulative distribution functions, and the cumulative distribution functions should be stored in registers, not a memory, there are many difficulties in actually implementing the histogram equalization algorithm using hardware.